Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations89320
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 MiB
Average record size in memory120.0 B

Variable types

Text3
Categorical1
Numeric11

Alerts

Freight_In_(tonnes) is highly overall correlated with Freight_Out_(tonnes) and 6 other fieldsHigh correlation
Freight_Out_(tonnes) is highly overall correlated with Freight_In_(tonnes) and 7 other fieldsHigh correlation
Freight_Total_(tonnes) is highly overall correlated with Freight_In_(tonnes) and 7 other fieldsHigh correlation
Mail_In_(tonnes) is highly overall correlated with Freight_In_(tonnes) and 7 other fieldsHigh correlation
Mail_Out_(tonnes) is highly overall correlated with Freight_Out_(tonnes) and 3 other fieldsHigh correlation
Mail_Total_(tonnes) is highly overall correlated with Freight_In_(tonnes) and 7 other fieldsHigh correlation
Passengers_In is highly overall correlated with Freight_In_(tonnes) and 6 other fieldsHigh correlation
Passengers_Out is highly overall correlated with Freight_In_(tonnes) and 6 other fieldsHigh correlation
Passengers_Total is highly overall correlated with Freight_In_(tonnes) and 6 other fieldsHigh correlation
Passengers_In has 10282 (11.5%) zeros Zeros
Freight_In_(tonnes) has 22229 (24.9%) zeros Zeros
Mail_In_(tonnes) has 50311 (56.3%) zeros Zeros
Passengers_Out has 9387 (10.5%) zeros Zeros
Freight_Out_(tonnes) has 21819 (24.4%) zeros Zeros
Mail_Out_(tonnes) has 47753 (53.5%) zeros Zeros
Passengers_Total has 6195 (6.9%) zeros Zeros
Freight_Total_(tonnes) has 16455 (18.4%) zeros Zeros
Mail_Total_(tonnes) has 38795 (43.4%) zeros Zeros

Reproduction

Analysis started2025-07-30 15:35:54.577911
Analysis finished2025-07-30 15:36:02.845762
Duration8.27 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Month
Text

Distinct482
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:02.985800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters535920
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan-85
2nd rowJan-85
3rd rowJan-85
4th rowJan-85
5th rowJan-85
ValueCountFrequency (%)
jun-00 314
 
0.4%
dec-99 311
 
0.3%
nov-99 311
 
0.3%
oct-99 311
 
0.3%
apr-00 307
 
0.3%
may-00 306
 
0.3%
feb-00 305
 
0.3%
mar-00 304
 
0.3%
oct-00 291
 
0.3%
jan-00 290
 
0.3%
Other values (472) 86270
96.6%
2025-07-30T19:06:03.222271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 89320
16.7%
9 37778
 
7.0%
0 31157
 
5.8%
1 27847
 
5.2%
e 22592
 
4.2%
a 22346
 
4.2%
J 22341
 
4.2%
u 22087
 
4.1%
8 20737
 
3.9%
2 17225
 
3.2%
Other values (23) 222490
41.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178640
33.3%
Lowercase Letter 178640
33.3%
Dash Punctuation 89320
16.7%
Uppercase Letter 89320
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22592
12.6%
a 22346
12.5%
u 22087
12.4%
c 15120
8.5%
n 14964
8.4%
r 14764
8.3%
p 14706
8.2%
b 7598
 
4.3%
t 7529
 
4.2%
v 7463
 
4.2%
Other values (4) 29471
16.5%
Decimal Number
ValueCountFrequency (%)
9 37778
21.1%
0 31157
17.4%
1 27847
15.6%
8 20737
11.6%
2 17225
9.6%
7 9371
 
5.2%
6 9038
 
5.1%
5 8852
 
5.0%
4 8388
 
4.7%
3 8247
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
J 22341
25.0%
M 14735
16.5%
A 14660
16.4%
F 7598
 
8.5%
D 7591
 
8.5%
O 7529
 
8.4%
N 7463
 
8.4%
S 7403
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
- 89320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 267960
50.0%
Latin 267960
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22592
 
8.4%
a 22346
 
8.3%
J 22341
 
8.3%
u 22087
 
8.2%
c 15120
 
5.6%
n 14964
 
5.6%
r 14764
 
5.5%
M 14735
 
5.5%
p 14706
 
5.5%
A 14660
 
5.5%
Other values (12) 89645
33.5%
Common
ValueCountFrequency (%)
- 89320
33.3%
9 37778
14.1%
0 31157
 
11.6%
1 27847
 
10.4%
8 20737
 
7.7%
2 17225
 
6.4%
7 9371
 
3.5%
6 9038
 
3.4%
5 8852
 
3.3%
4 8388
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 535920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 89320
16.7%
9 37778
 
7.0%
0 31157
 
5.8%
1 27847
 
5.2%
e 22592
 
4.2%
a 22346
 
4.2%
J 22341
 
4.2%
u 22087
 
4.1%
8 20737
 
3.9%
2 17225
 
3.2%
Other values (23) 222490
41.5%

AustralianPort
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size697.9 KiB
Sydney
25366 
Melbourne
18167 
Brisbane
14728 
Perth
9415 
Cairns
6974 
Other values (14)
14670 

Length

Max length22
Median length18
Mean length7.2402597
Min length5

Characters and Unicode

Total characters646700
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdelaide
2nd rowAdelaide
3rd rowAdelaide
4th rowAdelaide
5th rowAdelaide

Common Values

ValueCountFrequency (%)
Sydney 25366
28.4%
Melbourne 18167
20.3%
Brisbane 14728
16.5%
Perth 9415
 
10.5%
Cairns 6974
 
7.8%
Adelaide 6241
 
7.0%
Darwin 4542
 
5.1%
Gold Coast 1202
 
1.3%
Townsville 660
 
0.7%
Gold Coast/Coolangatta 503
 
0.6%
Other values (9) 1522
 
1.7%

Length

2025-07-30T19:06:03.330365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sydney 25366
27.6%
melbourne 18167
19.7%
brisbane 14728
16.0%
perth 9415
 
10.2%
cairns 6974
 
7.6%
adelaide 6241
 
6.8%
darwin 4542
 
4.9%
gold 1705
 
1.9%
coast 1257
 
1.4%
townsville 660
 
0.7%
Other values (13) 2958
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 99645
15.4%
n 71987
11.1%
r 55279
 
8.5%
y 50732
 
7.8%
d 40655
 
6.3%
a 37499
 
5.8%
b 33487
 
5.2%
i 33280
 
5.1%
l 29453
 
4.6%
S 25421
 
3.9%
Other values (27) 169262
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 550988
85.2%
Uppercase Letter 92516
 
14.3%
Space Separator 2693
 
0.4%
Other Punctuation 503
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 99645
18.1%
n 71987
13.1%
r 55279
10.0%
y 50732
9.2%
d 40655
7.4%
a 37499
 
6.8%
b 33487
 
6.1%
i 33280
 
6.0%
l 29453
 
5.3%
o 25389
 
4.6%
Other values (12) 73582
13.4%
Uppercase Letter
ValueCountFrequency (%)
S 25421
27.5%
M 18167
19.6%
B 14770
16.0%
P 9702
 
10.5%
C 9439
 
10.2%
A 6241
 
6.7%
D 4542
 
4.9%
G 1705
 
1.8%
T 778
 
0.8%
H 639
 
0.7%
Other values (3) 1112
 
1.2%
Space Separator
ValueCountFrequency (%)
2693
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 643504
99.5%
Common 3196
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 99645
15.5%
n 71987
11.2%
r 55279
 
8.6%
y 50732
 
7.9%
d 40655
 
6.3%
a 37499
 
5.8%
b 33487
 
5.2%
i 33280
 
5.2%
l 29453
 
4.6%
S 25421
 
4.0%
Other values (25) 166066
25.8%
Common
ValueCountFrequency (%)
2693
84.3%
/ 503
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 646700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 99645
15.4%
n 71987
11.1%
r 55279
 
8.5%
y 50732
 
7.8%
d 40655
 
6.3%
a 37499
 
5.8%
b 33487
 
5.2%
i 33280
 
5.1%
l 29453
 
4.6%
S 25421
 
3.9%
Other values (27) 169262
26.2%
Distinct200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:03.457165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length15
Mean length8.0665137
Min length3

Characters and Unicode

Total characters720501
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowAuckland
2nd rowBahrain
3rd rowBombay
4th rowFrankfurt
5th rowLondon
ValueCountFrequency (%)
auckland 3820
 
3.5%
singapore 3612
 
3.3%
denpasar 3265
 
3.0%
port 3155
 
2.9%
hong 3071
 
2.8%
kong 3071
 
2.8%
kuala 2744
 
2.5%
lumpur 2744
 
2.5%
tokyo 2614
 
2.4%
bangkok 2487
 
2.3%
Other values (223) 79435
72.2%
2025-07-30T19:06:03.682033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 82627
 
11.5%
n 63710
 
8.8%
o 62900
 
8.7%
r 42659
 
5.9%
e 42396
 
5.9%
u 41064
 
5.7%
i 37998
 
5.3%
g 27454
 
3.8%
l 25069
 
3.5%
s 22965
 
3.2%
Other values (47) 271659
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 589739
81.9%
Uppercase Letter 109959
 
15.3%
Space Separator 20698
 
2.9%
Other Punctuation 100
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 82627
14.0%
n 63710
10.8%
o 62900
10.7%
r 42659
 
7.2%
e 42396
 
7.2%
u 41064
 
7.0%
i 37998
 
6.4%
g 27454
 
4.7%
l 25069
 
4.3%
s 22965
 
3.9%
Other values (16) 140897
23.9%
Uppercase Letter
ValueCountFrequency (%)
A 10211
 
9.3%
S 9749
 
8.9%
H 9003
 
8.2%
B 8275
 
7.5%
D 7713
 
7.0%
L 7694
 
7.0%
M 6705
 
6.1%
N 6645
 
6.0%
K 6631
 
6.0%
C 6187
 
5.6%
Other values (16) 31146
28.3%
Space Separator
ValueCountFrequency (%)
20698
100.0%
Other Punctuation
ValueCountFrequency (%)
' 100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 699698
97.1%
Common 20803
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 82627
 
11.8%
n 63710
 
9.1%
o 62900
 
9.0%
r 42659
 
6.1%
e 42396
 
6.1%
u 41064
 
5.9%
i 37998
 
5.4%
g 27454
 
3.9%
l 25069
 
3.6%
s 22965
 
3.3%
Other values (42) 250856
35.9%
Common
ValueCountFrequency (%)
20698
99.5%
' 100
 
0.5%
( 2
 
< 0.1%
) 2
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 82627
 
11.5%
n 63710
 
8.8%
o 62900
 
8.7%
r 42659
 
5.9%
e 42396
 
5.9%
u 41064
 
5.7%
i 37998
 
5.3%
g 27454
 
3.8%
l 25069
 
3.5%
s 22965
 
3.2%
Other values (47) 271659
37.7%
Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:03.775277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.8416256
Min length2

Characters and Unicode

Total characters700414
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowNew Zealand
2nd rowBahrain
3rd rowIndia
4th rowGermany
5th rowUK
ValueCountFrequency (%)
new 14175
 
12.0%
zealand 10667
 
9.0%
usa 9394
 
7.9%
japan 5835
 
4.9%
indonesia 5681
 
4.8%
china 5086
 
4.3%
singapore 3612
 
3.0%
thailand 3227
 
2.7%
malaysia 3099
 
2.6%
hong 3071
 
2.6%
Other values (79) 54659
46.1%
2025-07-30T19:06:03.924685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 111854
16.0%
n 71786
 
10.2%
e 57556
 
8.2%
i 52969
 
7.6%
29186
 
4.2%
d 29101
 
4.2%
l 25382
 
3.6%
o 23952
 
3.4%
r 21096
 
3.0%
s 18581
 
2.7%
Other values (44) 258951
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 525633
75.0%
Uppercase Letter 142923
 
20.4%
Space Separator 29186
 
4.2%
Close Punctuation 1335
 
0.2%
Open Punctuation 1335
 
0.2%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 111854
21.3%
n 71786
13.7%
e 57556
10.9%
i 52969
10.1%
d 29101
 
5.5%
l 25382
 
4.8%
o 23952
 
4.6%
r 21096
 
4.0%
s 18581
 
3.5%
w 16255
 
3.1%
Other values (15) 97101
18.5%
Uppercase Letter
ValueCountFrequency (%)
S 17603
12.3%
N 15541
10.9%
A 15440
10.8%
U 14851
10.4%
Z 11362
 
7.9%
I 9653
 
6.8%
C 9602
 
6.7%
K 7233
 
5.1%
J 5835
 
4.1%
T 5830
 
4.1%
Other values (15) 29973
21.0%
Space Separator
ValueCountFrequency (%)
29186
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1335
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1335
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 668556
95.5%
Common 31858
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 111854
16.7%
n 71786
 
10.7%
e 57556
 
8.6%
i 52969
 
7.9%
d 29101
 
4.4%
l 25382
 
3.8%
o 23952
 
3.6%
r 21096
 
3.2%
s 18581
 
2.8%
S 17603
 
2.6%
Other values (40) 238676
35.7%
Common
ValueCountFrequency (%)
29186
91.6%
) 1335
 
4.2%
( 1335
 
4.2%
. 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 111854
16.0%
n 71786
 
10.2%
e 57556
 
8.2%
i 52969
 
7.6%
29186
 
4.2%
d 29101
 
4.2%
l 25382
 
3.6%
o 23952
 
3.4%
r 21096
 
3.0%
s 18581
 
2.7%
Other values (44) 258951
37.0%

Passengers_In
Real number (ℝ)

High correlation  Zeros 

Distinct18412
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4561.9724
Minimum0
Maximum90926
Zeros10282
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:03.998008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1128
median1290
Q34831
95-th percentile21184.2
Maximum90926
Range90926
Interquartile range (IQR)4703

Descriptive statistics

Standard deviation8564.7251
Coefficient of variation (CV)1.8774171
Kurtosis16.653361
Mean4561.9724
Median Absolute Deviation (MAD)1288
Skewness3.6218348
Sum4.0747537 × 108
Variance73354515
MonotonicityNot monotonic
2025-07-30T19:06:04.055388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10282
 
11.5%
1 673
 
0.8%
2 595
 
0.7%
4 390
 
0.4%
3 380
 
0.4%
5 306
 
0.3%
6 305
 
0.3%
7 245
 
0.3%
9 240
 
0.3%
8 238
 
0.3%
Other values (18402) 75666
84.7%
ValueCountFrequency (%)
0 10282
11.5%
1 673
 
0.8%
2 595
 
0.7%
3 380
 
0.4%
4 390
 
0.4%
5 306
 
0.3%
6 305
 
0.3%
7 245
 
0.3%
8 238
 
0.3%
9 240
 
0.3%
ValueCountFrequency (%)
90926 1
< 0.1%
88675 1
< 0.1%
84980 1
< 0.1%
84314 1
< 0.1%
83898 1
< 0.1%
83286 1
< 0.1%
82151 1
< 0.1%
81900 1
< 0.1%
81244 1
< 0.1%
80960 1
< 0.1%

Freight_In_(tonnes)
Real number (ℝ)

High correlation  Zeros 

Distinct51040
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.34267
Minimum0
Maximum6764.923
Zeros22229
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:04.111387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median14.962
Q3136.58375
95-th percentile884.2175
Maximum6764.923
Range6764.923
Interquartile range (IQR)136.58175

Descriptive statistics

Standard deviation407.41524
Coefficient of variation (CV)2.4346166
Kurtosis35.935575
Mean167.34267
Median Absolute Deviation (MAD)14.962
Skewness4.9471999
Sum14947047
Variance165987.18
MonotonicityNot monotonic
2025-07-30T19:06:04.172493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22229
 
24.9%
0.1 105
 
0.1%
0.01 72
 
0.1%
0.002 72
 
0.1%
0.001 71
 
0.1%
0.2 67
 
0.1%
0.003 57
 
0.1%
0.005 54
 
0.1%
0.004 54
 
0.1%
0.02 48
 
0.1%
Other values (51030) 66491
74.4%
ValueCountFrequency (%)
0 22229
24.9%
0.001 71
 
0.1%
0.002 72
 
0.1%
0.003 57
 
0.1%
0.004 54
 
0.1%
0.005 54
 
0.1%
0.006 33
 
< 0.1%
0.007 48
 
0.1%
0.008 43
 
< 0.1%
0.009 35
 
< 0.1%
ValueCountFrequency (%)
6764.923 1
< 0.1%
6751.778 1
< 0.1%
6725.072 1
< 0.1%
6615.954 1
< 0.1%
6493.122 1
< 0.1%
6469.027 1
< 0.1%
6368.841 1
< 0.1%
6267.136 1
< 0.1%
6260.926 1
< 0.1%
6253.855 1
< 0.1%

Mail_In_(tonnes)
Real number (ℝ)

High correlation  Zeros 

Distinct18481
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4427281
Minimum0
Maximum393.705
Zeros50311
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:04.445986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.703
95-th percentile47.2232
Maximum393.705
Range393.705
Interquartile range (IQR)1.703

Descriptive statistics

Standard deviation23.809746
Coefficient of variation (CV)3.1990617
Kurtosis38.716569
Mean7.4427281
Median Absolute Deviation (MAD)0
Skewness5.4492278
Sum664784.47
Variance566.90402
MonotonicityNot monotonic
2025-07-30T19:06:04.511427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50311
56.3%
0.001 310
 
0.3%
0.002 177
 
0.2%
0.004 150
 
0.2%
0.006 129
 
0.1%
0.005 120
 
0.1%
0.009 118
 
0.1%
0.003 115
 
0.1%
0.008 111
 
0.1%
0.01 104
 
0.1%
Other values (18471) 37675
42.2%
ValueCountFrequency (%)
0 50311
56.3%
0.001 310
 
0.3%
0.002 177
 
0.2%
0.003 115
 
0.1%
0.004 150
 
0.2%
0.005 120
 
0.1%
0.006 129
 
0.1%
0.007 97
 
0.1%
0.008 111
 
0.1%
0.009 118
 
0.1%
ValueCountFrequency (%)
393.705 1
< 0.1%
350.756 1
< 0.1%
349.801 1
< 0.1%
348.758 1
< 0.1%
340.111 1
< 0.1%
337.882 1
< 0.1%
323.973 1
< 0.1%
321.73 1
< 0.1%
317.128 1
< 0.1%
314.621 1
< 0.1%

Passengers_Out
Real number (ℝ)

High correlation  Zeros 

Distinct18364
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4491.4807
Minimum0
Maximum91078
Zeros9387
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:04.573739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1133
median1265
Q34698.25
95-th percentile20949.25
Maximum91078
Range91078
Interquartile range (IQR)4565.25

Descriptive statistics

Standard deviation8416.1984
Coefficient of variation (CV)1.8738137
Kurtosis16.48251
Mean4491.4807
Median Absolute Deviation (MAD)1260
Skewness3.5971485
Sum4.0117906 × 108
Variance70832395
MonotonicityNot monotonic
2025-07-30T19:06:04.631470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9387
 
10.5%
1 705
 
0.8%
2 595
 
0.7%
3 425
 
0.5%
4 412
 
0.5%
5 334
 
0.4%
6 294
 
0.3%
8 260
 
0.3%
7 258
 
0.3%
10 232
 
0.3%
Other values (18354) 76418
85.6%
ValueCountFrequency (%)
0 9387
10.5%
1 705
 
0.8%
2 595
 
0.7%
3 425
 
0.5%
4 412
 
0.5%
5 334
 
0.4%
6 294
 
0.3%
7 258
 
0.3%
8 260
 
0.3%
9 206
 
0.2%
ValueCountFrequency (%)
91078 1
< 0.1%
89209 1
< 0.1%
85562 1
< 0.1%
84510 1
< 0.1%
83168 1
< 0.1%
83117 1
< 0.1%
82766 1
< 0.1%
82560 1
< 0.1%
82242 1
< 0.1%
82129 1
< 0.1%

Freight_Out_(tonnes)
Real number (ℝ)

High correlation  Zeros 

Distinct50507
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.40685
Minimum0
Maximum4996.582
Zeros21819
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:04.687696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01175
median14.072
Q3118.1255
95-th percentile771.9082
Maximum4996.582
Range4996.582
Interquartile range (IQR)118.11375

Descriptive statistics

Standard deviation363.85378
Coefficient of variation (CV)2.4517317
Kurtosis25.177994
Mean148.40685
Median Absolute Deviation (MAD)14.072
Skewness4.5082317
Sum13255700
Variance132389.58
MonotonicityNot monotonic
2025-07-30T19:06:04.746120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21819
 
24.4%
0.1 78
 
0.1%
0.001 67
 
0.1%
0.01 61
 
0.1%
0.02 58
 
0.1%
0.002 52
 
0.1%
0.005 48
 
0.1%
0.015 46
 
0.1%
0.004 46
 
0.1%
0.006 45
 
0.1%
Other values (50497) 67000
75.0%
ValueCountFrequency (%)
0 21819
24.4%
0.001 67
 
0.1%
0.002 52
 
0.1%
0.003 41
 
< 0.1%
0.004 46
 
0.1%
0.005 48
 
0.1%
0.006 45
 
0.1%
0.007 35
 
< 0.1%
0.008 41
 
< 0.1%
0.009 40
 
< 0.1%
ValueCountFrequency (%)
4996.582 1
< 0.1%
4422.703 1
< 0.1%
4380.961 1
< 0.1%
4319.344 1
< 0.1%
4287.038 1
< 0.1%
4211.761 1
< 0.1%
4179.273 1
< 0.1%
4141.178 1
< 0.1%
4089.763 1
< 0.1%
4035.849 1
< 0.1%

Mail_Out_(tonnes)
Real number (ℝ)

High correlation  Zeros 

Distinct16195
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6284511
Minimum0
Maximum382.229
Zeros47753
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:04.801542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.55125
95-th percentile28.29005
Maximum382.229
Range382.229
Interquartile range (IQR)1.55125

Descriptive statistics

Standard deviation14.769693
Coefficient of variation (CV)3.1910659
Kurtosis61.998256
Mean4.6284511
Median Absolute Deviation (MAD)0
Skewness6.2600512
Sum413413.25
Variance218.14382
MonotonicityNot monotonic
2025-07-30T19:06:04.861142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47753
53.5%
0.001 221
 
0.2%
0.004 149
 
0.2%
0.002 139
 
0.2%
0.003 138
 
0.2%
0.005 133
 
0.1%
0.011 119
 
0.1%
0.008 119
 
0.1%
0.007 110
 
0.1%
0.006 110
 
0.1%
Other values (16185) 40329
45.2%
ValueCountFrequency (%)
0 47753
53.5%
0.001 221
 
0.2%
0.002 139
 
0.2%
0.003 138
 
0.2%
0.004 149
 
0.2%
0.005 133
 
0.1%
0.006 110
 
0.1%
0.007 110
 
0.1%
0.008 119
 
0.1%
0.009 104
 
0.1%
ValueCountFrequency (%)
382.229 1
< 0.1%
359.956 1
< 0.1%
358.67 1
< 0.1%
353.028 1
< 0.1%
323.718 1
< 0.1%
298.339 1
< 0.1%
288.633 1
< 0.1%
254.821 1
< 0.1%
249.821 1
< 0.1%
246.197 1
< 0.1%

Passengers_Total
Real number (ℝ)

High correlation  Zeros 

Distinct25835
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9053.4531
Minimum0
Maximum170469
Zeros6195
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:04.921278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1284
median2573
Q39614
95-th percentile42050.05
Maximum170469
Range170469
Interquartile range (IQR)9330

Descriptive statistics

Standard deviation16905.519
Coefficient of variation (CV)1.8673007
Kurtosis16.311046
Mean9053.4531
Median Absolute Deviation (MAD)2555
Skewness3.5893243
Sum8.0865443 × 108
Variance2.8579659 × 108
MonotonicityNot monotonic
2025-07-30T19:06:04.978940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6195
 
6.9%
1 713
 
0.8%
2 652
 
0.7%
3 430
 
0.5%
4 382
 
0.4%
5 348
 
0.4%
6 317
 
0.4%
7 270
 
0.3%
8 255
 
0.3%
10 235
 
0.3%
Other values (25825) 79523
89.0%
ValueCountFrequency (%)
0 6195
6.9%
1 713
 
0.8%
2 652
 
0.7%
3 430
 
0.5%
4 382
 
0.4%
5 348
 
0.4%
6 317
 
0.4%
7 270
 
0.3%
8 255
 
0.3%
9 228
 
0.3%
ValueCountFrequency (%)
170469 1
< 0.1%
166362 1
< 0.1%
165111 1
< 0.1%
162036 1
< 0.1%
161346 1
< 0.1%
160623 1
< 0.1%
160620 1
< 0.1%
160562 1
< 0.1%
159558 1
< 0.1%
158788 1
< 0.1%

Freight_Total_(tonnes)
Real number (ℝ)

High correlation  Zeros 

Distinct59370
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.74952
Minimum0
Maximum9889.553
Zeros16455
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:05.039820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.781
median43.536
Q3275.00325
95-th percentile1677.7757
Maximum9889.553
Range9889.553
Interquartile range (IQR)274.22225

Descriptive statistics

Standard deviation728.10837
Coefficient of variation (CV)2.3059683
Kurtosis24.600666
Mean315.74952
Median Absolute Deviation (MAD)43.536
Skewness4.3561061
Sum28202747
Variance530141.8
MonotonicityNot monotonic
2025-07-30T19:06:05.098391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16455
 
18.4%
0.1 65
 
0.1%
0.01 61
 
0.1%
0.001 58
 
0.1%
0.002 55
 
0.1%
0.004 43
 
< 0.1%
0.02 42
 
< 0.1%
0.009 41
 
< 0.1%
0.17 40
 
< 0.1%
0.012 39
 
< 0.1%
Other values (59360) 72421
81.1%
ValueCountFrequency (%)
0 16455
18.4%
0.001 58
 
0.1%
0.002 55
 
0.1%
0.003 38
 
< 0.1%
0.004 43
 
< 0.1%
0.005 38
 
< 0.1%
0.006 38
 
< 0.1%
0.007 32
 
< 0.1%
0.008 36
 
< 0.1%
0.009 41
 
< 0.1%
ValueCountFrequency (%)
9889.553 1
< 0.1%
9646.309 1
< 0.1%
9561.433 1
< 0.1%
9315.425 1
< 0.1%
9279.379 1
< 0.1%
9196.365 1
< 0.1%
9026.831 1
< 0.1%
8959.197 1
< 0.1%
8955.371 1
< 0.1%
8728.065 1
< 0.1%

Mail_Total_(tonnes)
Real number (ℝ)

High correlation  Zeros 

Distinct23138
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.071179
Minimum0
Maximum566.993
Zeros38795
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:05.152643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.105
Q34.932
95-th percentile74.1866
Maximum566.993
Range566.993
Interquartile range (IQR)4.932

Descriptive statistics

Standard deviation34.345207
Coefficient of variation (CV)2.8452238
Kurtosis29.928787
Mean12.071179
Median Absolute Deviation (MAD)0.105
Skewness4.7894984
Sum1078197.7
Variance1179.5932
MonotonicityNot monotonic
2025-07-30T19:06:05.207478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38795
43.4%
0.001 263
 
0.3%
0.002 140
 
0.2%
0.004 138
 
0.2%
0.005 132
 
0.1%
0.006 115
 
0.1%
0.011 115
 
0.1%
0.012 110
 
0.1%
0.003 110
 
0.1%
0.013 107
 
0.1%
Other values (23128) 49295
55.2%
ValueCountFrequency (%)
0 38795
43.4%
0.001 263
 
0.3%
0.002 140
 
0.2%
0.003 110
 
0.1%
0.004 138
 
0.2%
0.005 132
 
0.1%
0.006 115
 
0.1%
0.007 100
 
0.1%
0.008 105
 
0.1%
0.009 89
 
0.1%
ValueCountFrequency (%)
566.993 1
< 0.1%
547.83 1
< 0.1%
541.348 1
< 0.1%
489.908 1
< 0.1%
483.767 1
< 0.1%
474.902 1
< 0.1%
456.677 1
< 0.1%
444.414 1
< 0.1%
441.447 1
< 0.1%
429.292 1
< 0.1%

Year
Real number (ℝ)

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.0775
Minimum1985
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:05.264903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1987
Q11994
median2001
Q32013
95-th percentile2022
Maximum2025
Range40
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.222014
Coefficient of variation (CV)0.0056023862
Kurtosis-1.0734928
Mean2003.0775
Median Absolute Deviation (MAD)9
Skewness0.25231677
Sum1.7891489 × 108
Variance125.9336
MonotonicityIncreasing
2025-07-30T19:06:05.324637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2000 3502
 
3.9%
1999 3407
 
3.8%
1998 3162
 
3.5%
1997 3157
 
3.5%
1996 3035
 
3.4%
2001 2970
 
3.3%
1995 2807
 
3.1%
2002 2769
 
3.1%
1993 2656
 
3.0%
1994 2600
 
2.9%
Other values (31) 59255
66.3%
ValueCountFrequency (%)
1985 1944
2.2%
1986 2224
2.5%
1987 2284
2.6%
1988 2361
2.6%
1989 2369
2.7%
1990 2471
2.8%
1991 2281
2.6%
1992 2469
2.8%
1993 2656
3.0%
1994 2600
2.9%
ValueCountFrequency (%)
2025 375
 
0.4%
2024 2125
2.4%
2023 1923
2.2%
2022 1556
1.7%
2021 1271
1.4%
2020 1373
1.5%
2019 2219
2.5%
2018 2240
2.5%
2017 2106
2.4%
2016 1949
2.2%

Month_num
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4989588
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size697.9 KiB
2025-07-30T19:06:05.377434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4724299
Coefficient of variation (CV)0.53430557
Kurtosis-1.2299644
Mean6.4989588
Median Absolute Deviation (MAD)3
Skewness-0.0020066336
Sum580487
Variance12.057769
MonotonicityNot monotonic
2025-07-30T19:06:05.423820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7611
8.5%
2 7598
8.5%
12 7591
8.5%
10 7529
8.4%
11 7463
8.4%
3 7461
8.4%
9 7403
8.3%
7 7377
8.3%
8 7357
8.2%
6 7353
8.2%
Other values (2) 14577
16.3%
ValueCountFrequency (%)
1 7611
8.5%
2 7598
8.5%
3 7461
8.4%
4 7303
8.2%
5 7274
8.1%
6 7353
8.2%
7 7377
8.3%
8 7357
8.2%
9 7403
8.3%
10 7529
8.4%
ValueCountFrequency (%)
12 7591
8.5%
11 7463
8.4%
10 7529
8.4%
9 7403
8.3%
8 7357
8.2%
7 7377
8.3%
6 7353
8.2%
5 7274
8.1%
4 7303
8.2%
3 7461
8.4%

Interactions

2025-07-30T19:06:01.995879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.149398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.863399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.491587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.052751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.579924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.092769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.659615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.214940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.880183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.417887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.039365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.249926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.910056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.537666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.095834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.625364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.139813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.705013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.260368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.926309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.464852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.088686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.349855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.958042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.586869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.145132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.673443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.220422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.757253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.436185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.978436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.519026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.136542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.437762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.112547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.636936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.198573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.724401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.272750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.809189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.489823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.028037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.572692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.178021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.491801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.156866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.684031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.243876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.768619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.318624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.856586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.535855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.073703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.619573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.223276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.543384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.203418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.731328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.292598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.810641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.366947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.904811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.583318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.120999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.696738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.271232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.594750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.252393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.789960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.343079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.860130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.415273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.956714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.636341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.172928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.749178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.318492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.645985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.302531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.844674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.391727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.907484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.467133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.009844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.686231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.223784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.802094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.363123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.699861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.347595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.895447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.438555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.955392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.512933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.060414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.734227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.272069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.849492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.409084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.744969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.394769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.947457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.484082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.999556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.562178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.111139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.781434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.316186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.897353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:02.458176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:56.819481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:57.444927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.002716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:58.536518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.049335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:05:59.612247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.166244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:00.833324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.371547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-30T19:06:01.948170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-07-30T19:06:05.462671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AustralianPortFreight_In_(tonnes)Freight_Out_(tonnes)Freight_Total_(tonnes)Mail_In_(tonnes)Mail_Out_(tonnes)Mail_Total_(tonnes)Month_numPassengers_InPassengers_OutPassengers_TotalYear
AustralianPort1.0000.0780.0630.0740.0820.0550.0810.0000.0820.0840.0850.126
Freight_In_(tonnes)0.0781.0000.8080.9540.6560.4960.6380.0140.7240.7180.7220.289
Freight_Out_(tonnes)0.0630.8081.0000.9140.6360.5420.6410.0090.7350.7430.7420.226
Freight_Total_(tonnes)0.0740.9540.9141.0000.6560.5130.6440.0130.7160.7180.7180.282
Mail_In_(tonnes)0.0820.6560.6360.6561.0000.6430.8820.0050.5680.5680.5710.099
Mail_Out_(tonnes)0.0550.4960.5420.5130.6431.0000.8570.0090.4680.4700.471-0.048
Mail_Total_(tonnes)0.0810.6380.6410.6440.8820.8571.0000.0110.5600.5620.5640.053
Month_num0.0000.0140.0090.0130.0050.0090.0111.000-0.0030.0070.002-0.014
Passengers_In0.0820.7240.7350.7160.5680.4680.560-0.0031.0000.9720.9910.359
Passengers_Out0.0840.7180.7430.7180.5680.4700.5620.0070.9721.0000.9920.365
Passengers_Total0.0850.7220.7420.7180.5710.4710.5640.0020.9910.9921.0000.361
Year0.1260.2890.2260.2820.099-0.0480.053-0.0140.3590.3650.3611.000

Missing values

2025-07-30T19:06:02.529512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-30T19:06:02.663739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MonthAustralianPortForeignPortCountryPassengers_InFreight_In_(tonnes)Mail_In_(tonnes)Passengers_OutFreight_Out_(tonnes)Mail_Out_(tonnes)Passengers_TotalFreight_Total_(tonnes)Mail_Total_(tonnes)YearMonth_num
0Jan-85AdelaideAucklandNew Zealand151342.1670.31198518.7040.924249860.8711.23519851
1Jan-85AdelaideBahrainBahrain120.0000.00050.0330.000170.0330.00019851
2Jan-85AdelaideBombayIndia70.0000.00050.0000.000120.0000.00019851
3Jan-85AdelaideFrankfurtGermany1150.0090.0001710.0000.2482860.0090.24819851
4Jan-85AdelaideLondonUK15672.8000.000147210.6182.487303913.4182.48719851
5Jan-85AdelaideMuscatOman170.0000.000140.1000.000310.1000.00019851
6Jan-85AdelaideRomeItaly790.0050.000440.0000.0001230.0050.00019851
7Jan-85AdelaideSingaporeSingapore249637.3450.0002037133.2030.1124533170.5480.11219851
8Jan-85BrisbaneAbu DhabiUnited Arab Emirates00.0000.00030.0000.00030.0000.00019851
9Jan-85BrisbaneAucklandNew Zealand7157223.2580.671565233.0323.21812809256.2903.88919851
MonthAustralianPortForeignPortCountryPassengers_InFreight_In_(tonnes)Mail_In_(tonnes)Passengers_OutFreight_Out_(tonnes)Mail_Out_(tonnes)Passengers_TotalFreight_Total_(tonnes)Mail_Total_(tonnes)YearMonth_num
89310Feb-25SydneyTokyoJapan35130984.61171.65931216553.56210.333663461538.17381.99220252
89311Feb-25SydneyTongatapuTonga5160.0000.0004970.0930.00710130.0930.00720252
89312Feb-25SydneyTorontoCanada14710.0000.00015180.0000.24529890.0000.24520252
89313Feb-25SydneyVancouverCanada8784193.3814.1497628254.55850.77516412447.93954.92420252
89314Feb-25SydneyWellingtonNew Zealand102317.3560.00091835.9710.0821941413.3270.08220252
89315Feb-25SydneyWuhanChina225523.7441.54451246.9320.038276770.6761.58220252
89316Feb-25SydneyXiamenChina7474299.4900.6104456194.7800.00011930494.2700.61020252
89317Feb-25SydneyXi'anChina113630.7110.00029549.2840.003143179.9950.00320252
89318Feb-25SydneyZhengzhouChina162049.73813.4306902.5200.000231052.25813.43020252
89319Feb-25Toowoomba WellcampHong KongHong Kong (SAR)031.8160.000093.0680.0000124.8840.00020252